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This paper introduces an interactive approach to support multi-criteria decision analysis of project portfolios. In high-scale strategic decision domains, scientific studies suggest that the Decision Maker (DM) can find help by using many-objective optimisation methods, which are supposed to provide values in the decision variables that generate high-quality solutions. Even so, DMs usually wish to explore the possibility of reaching some levels of benefits in some objectives. Consequently, they should repeatedly run the optimisation method. However, this approach cannot perform well – in an interactive way – for large instances under the presence of many objective functions. We present a mathematical model that is based on compromise programming and fuzzy outranking to aid DMs in analysing multi-criteria project portfolios on the fly. This approach allows relaxing the problem of rapidly optimising portfolios while preserving the beneficial properties of the DM’s preferences expressed by outranking relations. Our model supports the decision analysis on two instance benchmarks: for the first one, a better compromise solution was generated 84% of the runs; for the second one, this ranged from 93% to 97%. Our model was also applied to a real-world problem involving social projects, obtaining satisfactory results.

Decisions are made by those who are authorized and responsible decision makings for an institution or organization. Many decisions involve stakeholders who are individuals and organizations who can be affected by the future consequences of these decisions, including decision-making on Internet data quota assistance distributed by the Ministry of Education and Culture to support students' online learning from home (LFH). This Assistance Policy is very appropriate and well provided during the Covid-19 Pandemic. However, in an effort to optimize and objectify distribution so that it is right on target, this research is proposed solutions for applying the analysis decision-making mode using Multi-criteria Decision-making (MCDM). The criteria used include the amount of internet data consumption, Academic credits, courses, and student's economic capacity. The criteria weighting method uses Rank-order Centroid (ROC) and reference point MOORA for determining the best alternative. Â The results showed that the implementation of the MCDM ROC-Reference MOORA method affected the preference value and alternative ranking of internet data quota assistance. This shows that the criteria weight value and the analysis method are critical aspects in decision-making. Differences in weight, even the slightest change in weight, can drastically change the final decision

Purpose Determining the relevant information using financial measures is of great interest for various stakeholders to analyze the performance of the firm. This paper aims at identifying these financial measures (ratios) which critically affect the firm performance. The authors specifically focus on discovering the most prominent ratios using a two-step process. First, the authors use an exploratory factor analysis to identify the underlying dimensions of these ratios, followed by predictive modeling techniques to identify the potential relationship between measures and performance. Design/methodology/approach The study uses data of 25 financial variables for a sample of 1923 Indian manufacturing firms which exist continuously between 2011 and 2018. For prediction models, four popular decision tree algorithms [Chi-squared automatic interaction detector (CHAID), classification and regression trees (C&RT), C5.0 and quick, unbiased, efficient statistical tree (QUEST)] were investigated, and the information fusion-based sensitivity analyses were performed to identify the relative importance of these input measures. Findings Results show that C5.0 and CHAID algorithms produced the best predictive results. The fusion sensitivity results find that net profit margin and total assets turnover rate are the most critical factors determining the firm performance in an Indian manufacturing context. These findings may enable managers in their decision-making process and also have vital implications for investors in assessing the performance of the firm. Originality/value To the best of the authors’ knowledge, the current paper is the first to address the application of decision tree algorithms to predict the performance of manufacturing firms in an emerging economy such as India, with the latest data. This practical perspective helps the organizations in managing the critical parameters for the firm’s growth.

Objectives: To experiment with new approaches of collaboration in healthcare delivery, local authorities implement new models of care. Regarding the local decision-context of these models, Multi-Criteria Decision Analysis (MCDA) may be of added value to Cost-Utility Analysis (CUA), as it covers a wider range of outcomes. This study compares the two methods using a side-by-side application. Methods: A new Dutch model of care, Primary Care Plus (PC+), was used as case study to compare the results of CUA and MCDA. Data of patients referred to PC+ or care-as-usual was retrieved by questionnaires and administrative databases with a three-month follow-up. Propensity score matching together with generalised linear regression models was used to reduce confounding. Univariate and probabilistic sensitivity analyses were performed to explore uncertainty in the results. Results: Although both methods indicated PC+ as dominant alternative, complementary differences were observed. MCDA provided additional evidence that PC+ improved access to care (standardised performance score of 0.742 versus 0.670), improvement in health-related quality of life was driven by the psychological well-being component (standardised performance score of 0.710 versus 0.704), estimated the budget required for the PC+ to be affordable in addition to preferable (€521.42 per patient). Additionally, MCDA was less sensitive to the utility measures used. Conclusions: MCDA may facilitate an auditable and transparent evaluation of new models of care by providing additional information on a wider range of outcomes and incorporating affordability. However, more effort is needed to increase the usability of MCDA among local decision makers.

Choosing a strategy for electronic conflicts in the field of action and situations similar to the problem scenario is a matter of experience and will not necessarily be the best possible choice. Therefore, the importance of providing a reasonable and measurable decision-making method in choosing a strategy in real conditions is quite clear. One of the points that can be deduced from the study of common methods and techniques in electronic warfare is that none of these methods are considered absolute winners and the holder can’t consider himself the undisputed winner of the field. Because there is more or less the opposite of any technique. Using a method or tool in a situation may be very effective, but with a change of method or change of tool by the competitor, it becomes a weakness. Therefore, in this battle, the correct and timely use of each technique, and with full knowledge of the opponent and in accordance with the actions performed by him can be considered a factor of success and all useful tools in this field can be used. One of the most useful tools in the field of strategic decision analysis is game theory. With the help of this mathematical tool, the decision-making process between two or more decision-makers can be examined and the results can be analyzed. It is also possible to observe the effect of various parameters involved in the result of the problem, and to plan the result in the desired direction. In this paper, selecting the best response in different stages of the conflict to solve various such problems is achieved and the main purpose is to present a new approach to adopt a strategy and select the radar and disruptive coping technique in case of incomplete information from each other, using a method It is mathematical and algorithmic, so that we have a better performance in the benefit function of the parties.

Identification of hospital sites and their ranking is important for the planning and development of any country's health infrastructure. The site selection problem is a typical multi-criteria decision making problem involving multiple stakeholders and their interests. Multi-Criteria Decision Analysis (MCDA) is a promising approach to solve a location-based problem due to the constitution of various criteria involved in decision making. In this research, eleven criterion are chosen which are classified under three main criteria; socio-economic, geographical and environmental. This research aims to identify the appropriate MCDA method for the selection of a new hospital sites. Here, two MCDA methods named Analytical Hierarchy Process (AHP) and Fuzzy AHP (FAHP) are used. Further, Geographical Information System (GIS) based MCDA methodology is proposed in this paper. The results obtained with both AHP and FAHP methods are compared. This comparison is based on criterion rankings, proposed hospital locations and sensitivity analysis. The main difference in results is shown in the result of sensitivity analysis in which constant variation in site ranking is obtained when weight change analysis is performed using AHP. The FAHP result shows only one variation in site ranking after a change in weight from +10 to +20%. The result suggests that FAHP may be a better approach to the hospital site selection problem.

This paper presents a comprehensive review of the methodological frameworks and criteria used for municipal solid waste landfill selection. The review is based on 89 scientific papers published in peer reviewed journals from 1983 onwards. The descriptive statistical analyses of the reviewed papers consider temporal, location-based quantitative, and qualitative factors. The papers considered are classified by the country where the case studies were carried out, and the qualitative ranking is performed according to the number of citations. Afterwards, the employed methods and criteria for landfill site selection were extensively analyzed and classified. The summary of the conducted analyses shows that Geographical Information Systems (GIS), either as an individual technique or in combination with other approaches are extensively used. Weighted linear combination is the most frequently applied multi-criteria decision analysis method for ranking of alternatives. The analytical hierarchy process is the dominating method for weighting the criteria. A combination of GIS with Remote Sensing techniques is used in several landfill siting studies as a more appealing approach, due to the capability of real-time data updates. The evaluations of the landfill siting criteria indicate that the most frequent main criterion is environmental, followed by economic and social criteria, while the most preferred sub-criteria is distance to the surface waters. These findings and classifications are beneficial to both, the researchers and decision makers, while serving as a support to the complex and difficult process of real-world landfill site selection.

Many regions and urban areas are becoming more engaged in selecting the optimum future clean energy technology mix to best fit their local power requirements. At the feasibility stage, such analysis is difficult to perform quantitatively due to the substantial uncertainties associated with many of the key influencing criteria. Moreover, changing climate means the renewable energy mix most suited to many regions is also changing as local climates progressively change. A protocol is proposed and evaluated for conducting qualitative multi-criteria decision analysis (MCDA) of multiple clean energy alternatives, suitable for specific regional conditions, using the TOPSIS method. This begins with linguistic assessments of a large number of pertinent criteria (50 or more) taking into account the diverse preferences of the many stakeholders involved. The linguistic assessments are inverted to integer number, fuzzy and intuitionistic fuzzy scoring (IFS) systems. The IFS method is shown to integrate uncertainty in a more flexible way. The fuzzy and IFS TOPSIS methods adjust their impact matrices with three weight factors: (1) objective weights derived from calculated entropy for each criteria, (2) subjective weights associated with preferences expressed by individual representative stakeholders; (3) subjective weights applied to balance the preferences among stakeholder groups. The three methods are applied using regionally specific case studies to illustrate and compare the clean energy rankings they select for the conditions associated with the specific region evaluated. Fuzzy and IFS scoring systems generate slightly different rankings as they capture uncertainty in different ways.

The aim of the article was to present the model of integration and cooperation (hybridization) of selected methods of decision support as a specific research procedure. The model is the basis for the construction of the so-called decision-making dashboard, which is the main module of the academic, prototype version of the multi-criteria decision support system – DSS 3.0 [1], [2]. Three methods were hybridized: AHP/ANP and ELECTRE TRI included in the area of multi-criteria decision analysis and the method of identification and econometric evaluation based on multiple regression analysis. Due to the limited volume of the study, the description of the algorithms of these methods was abandoned. The focus was on defining the set of input data common to the above-mentioned methods and on the description of the integration process of the results of the decision analysis obtained with their use, indicating the effects of their synergy. The properties of the model are illustrated on a practical example of employee team assessment (case study). The decision making dashboard provided answers to some interesting questions. How is the ranking of employees shaped? What is the overall assessment of a given employee (or candidate for a position) compared to the results of the entire staff? What is the distribution of the number of assignments of the ranked employees to the established classes (profiles) of multi-criteria assessment? Are the ranking favourites in the group of the highest, average or poorly rated employees? What is the ratio of the level of employees’ remuneration to the ratings obtained in the ranking?

Integrating a broad range of information types and finding trade-offs between conflicting goals is a challenge in global supply chain network design (GSCND). Effective decision support systems (DSS) should be user-friendly, provide transparency, and support human judgement. There is a wide range of optimisation models that aim to improve the outcome of network design decisions. However, their practical performance often remains unknown, as their implementation into the managerial decision process is largely neglected. Such theory-driven models usually focus on single aspects of the decision, without being able to accommodate the practical problem comprehensively. We employ the CIMO approach to resolve the issue and contribute by showing how an integration involving these methods can be useful for managers once the proper knowledge transfer has been effectuated. An innovative decision support framework, which combines mixed-integer linear programming, the Analytical Hierarchy Process, and the Pareto front is created and analysed during a case study in the med-tech industry. Results show that the framework accommodates managerial experience, integrates qualitative as well as quantitative criteria, and provides transparency over the entire range of efficient solutions. The framework and application results contribute towards the development of more flexible and easy-to-use decision support systems for GSCND.

Evaluation of an educational program is a complex task and its solution requires the analysis of data obtained on the basis of relevant to both quantitative and qualitative research. The collection of these data should be carried out using properly developed key performance indicators. The paper shows that such a task can be successfully solved using the TOPSIS method of multi-criteria decision analysis well known in information systems theory. The evaluation criteria can be positive or negative. The higher the rating value for a positive criterion, the better the alternative decision, while for the negative criterion the smaller the corresponding rating value, the better the alternative. The TOPSIS method is based on the concept that the best solution among the available alternatives is the one that has the minimum distance from the positive ideal solution and, at the same time, the maximum distance from the negative ideal solution. The paper presents both positive and negative criteria developed to evaluate the performance of educational programs. For the data relevant to the above criteria there are used the appropriate data of the four higher educational programs of Gori State Teaching University. Using the TOPSIS method the rating evaluation of the programs was carried out. The paper indicates that in the same way it is possible to assess the performance of one of the educational programs using data obtained at different intervals in relation to this program, and thereby determine the degree of success or failure of the program by comparing the results.

Shipping logistics is one of the very important criteria which can directly and indirectly affect the economy and GDP of any country. Shipping logistics depends on various factors which have been addressed by several authors in their previous studies. Studies in this literature are focused on selecting the most impactful factors among all the criteria. Methods used in this literature are fuzzy Analytical hierarchy process (AHP) and fuzzy Technique for order of Preference by Similarity to Ideal Solution (TOPSIS) for multi-criteria decision analysis. These methods also helped in this literature to develop a new hybrid method “fuzzy TOPSIS AHP”. There have been no studies involving maritime logistics with comparative analysis of multi-criteria decision making i.e., fuzzy AHP and fuzzy TOPSIS AHP. The literature involved large number of expert opinions on the factor prioritization of maritime logistics. Factors selected for prioritization are Environmental Sustainability, Supply and Demand, Operations and Port Selection. However, the research showed that the comparative analysis of the results was quite opposite to one another and proposed a new way for researchers to use the hybrid method of fuzzy TOPSIS AHP method in future research. The study aimed to improve the existing maritime model which can help professionals to get connected with the maritime logistics firms. The study also aims to contribute this model for researchers in their study related to maritime logistics.

In “Deadlines, Timing, and the Search for Alternatives,” S. Zorc and I. Tsetlin consider decentralized matching markets like the job market for MBAs. They show that exploding offers (ones with a very short deadline) are often optimal. The benefit of those offers is to prevent the other party’s search for alternatives. However, the main argument in favor of longer deadlines is that people who already hold an offer become more selective when deciding whether to accept other alternatives (the acceptance deterrence effect). Thus, the goal of curtailing search can also be accomplished through increasing the other party’s selectivity instead of preventing them from searching altogether. A practical prescription for offer makers is to use exploding offers when acceptance deterrence is weak (e.g., when the other party’s alternatives will not change) and longer deadlines when it is strong (e.g., when you are able to react to the other party’s other offers).

Background. Reviewing drugs to determine coverage or reimbursement level is a complex process that involves significant time and expertise. Review boards gather evidence from the submission provided, input from clinicians and patients, and results of clinical and economic reviews. This information consists of assessments on multiple criteria that often conflict with one another. Multiple-criteria decision analysis (MCDA) includes methods to address complex decision making problems with conflicting objectives and criteria. We propose an MCDA approach that infers a utility model based on reviews of previously submitted drugs. Methods. We use a recent extension of the UTilitiés Additives DIScriminantes approach, UTADISGMS. This disaggregation approach deconstructs a portfolio of elements such as a set of drugs that have been reviewed and for which a decision has been made. It derives global and marginal utility functions that are consistent with the preferences exhibited by the review boards in their recommendations. We apply the method to oncology drugs reviewed in Canada between 2011 and 2017. We also illustrate how to conduct scenario analyses and predict the coverage decisions for new drugs. Results. Applying the method yields a utility value for each submission along with a set of thresholds that partition the utility values based on the submission outcomes. Scenario analyses illustrate the predictive ability of the method. Conclusion. Preference disaggregation is an indirect way of eliciting an additive global utility value function. It requires less of a cognitive effort from the decision making bodies because it infers preferences from the data rather than relying on direct assessments of model parameters. We illustrate how it can be applied to validate existing decisions and to predict the recommendation of a new drug.

Selecting a supplier for highly technical and expensive equipment as a weather radar is a very demanding process and a critical management task for procurement team. For such problems, the use of multiple criteria analysis is welcome. This paper introduces a decision aid model for weather radar supplier selection using a multi-attribute approach for public purchase procedures. As a model application, we developed the framework on a real public procurement to expand the weather radar network in Brazil, where two attributes were in conflict (cost and technical). The proposed model was demonstrated to be very useful in understanding the trade-off between cost and technical attributes, based on the stakeholders’ preferences. The model also supported the decision-maker in the negotiation process to receive better commercial or technical proposals from the buyers’ perspective. Based on these findings, it is advocated that for highly specialised equipment, a single attribute method centred on the lowest-cost bid is no longer supportive and robust enough in contemporary supplier selection. Modelling a multi-attribute decision process could also be a guide to improve interaction with expert and management teams in future weather radar selection evaluations.

Purpose Safety data sheets are documents developed by chemical manufacturers to identify and label hazardous materials. The occupational safety and health administration regulations state that employers must make safety data sheets available for employees. When firms use hundreds of chemicals, tracking their safety data sheets becomes difficult. Safety Data Sheet Management Systems are developed to track safety data sheets. This paper aims to propose a multi-attribute decision-making framework for selecting a Safety Data Sheet Management System. Design/methodology/approach A total of 12 attributes are proposed based on a real-life project conducted at a firm in New York and the software selection models existed in the literature. Fuzzy technique for order of preference by similarity to ideal solution is used to assess the proposed attributes and alternatives. A case study and sensitivity analysis are conducted to show the robustness of the proposed model. Fuzzy analytical hierarchy process is used for validation. Findings Safety Data Sheet Management System is important for firms to track and manage safety data sheets. The proposed framework is practical and easy to implement. Practical implications The proposed decision model is useful for firms to select a proper Safety Data Sheet Management System. The system developers can use the model to update their systems. Originality/value This paper develops a new multi-attribute decision-making model for selecting a Safety Data Sheet Management System. To the best of the authors’ knowledge, no previous study has developed such a model.

Purpose Because that the crime in a wide way impacts the life of the people in the big metropolis, researchers have treated the question from several angles. The purpose of the paper, under the umbrella of operational research, is to develop a model of the ordering of police strategies, in the fight against crime in general, according to a certain criminal demand. Design/methodology/approach For the construction of the impact matrix of the strategies under the reduction of crime rates, considering a portfolio of crimes, a questionnaire applied to specialists was used. In a second moment, defined the criteria and strategies to be ordered, the multicriteria ELECTRE IV method was used, which with the help of the J-Electre software emulated the systematized data in the impact matrix and produced the final ordering of the most efficient strategies, in the fight against crime, in the perception of decision-makers. Findings As a result, the research revealed that policing strategies directed at solving specific crimes are the most effective in the perception of decision-makers after the emulation of data with the ELECTRE IV method. Research limitations/implications As research implications, it can be inferred that the use of multicriteria methods in the modeling of problems in the area of public security can contribute to rationalization of the use of the means available in the fight against crime in large cities. The research showed that it is possible to use customized policing strategies to a certain reality. Practical implications The method presented in this research is directly related to the major strategies: problem-oriented policing and hot spot policing. This method allows public safety managers to consider the possibility of combining different law enforcement strategies in each context. In this sense, the use of the multiple-criteria decision analysis (MCDA) (ELECTRE IV) method allows the evaluation of a large set of alternatives according to a set of established criteria, speeding up the process and reducing subjectivity, allowing the manager to analyze several scenarios with greater clarity and impartiality and choosing an alternative that best solves the proposed problem. The expected result is the rationalization of the available means applied in the search for the reduction of crime rates. Social implications The customization of policing strategies, according to criminal demands, implies the efficient way to reduce criminal charges. Reducing criminal rates enables the development of the local economy, tourism and the quality of life of people by exercising their freedom to the full. Originality/value The originality lies in filling a gap in the literature with the elaboration of the impact matrix of policing strategies in reducing criminal indices, and in their associated use in ordering strategies through a multicriteria method.

Multi-criteria decision analysis (MCDA) is a widely adopted tool, which is regarded as a suitable set of methods to evaluate sustainability in a variety of energy applications. The MCDA is usually performed employing a deterministic approach. Such an approach is rather not adequate to include model uncertainties connected with both: decision maker preferences (criteria weights) and process input data. By considering the stochastic nature of uncertainties, it is possible to generate results that can easily be analyzed statistically. In addition, such an approach allows investigating the consequences of the resulting uncertainty of multi-attribute aggregation on the final decision. In this study, we define, analyze and compare four different technologies of biodiesel production from used cooking oil (UCO) using probabilistic MCDA. Among the criteria, we consider energy, economic, environmental, as well as social aspects. To evaluate the uncertainty of the final results, we propose a novel method, coupling Monte Carlo simulation and method of data reconciliation. Firstly, we present the results of the deterministic case, in which we assume the exact values of criteria and the preferences. Besides, we investigate on a sensitivity of the results to identify the major factors affecting the economic, energy, environmental and social viability of biodiesel production. Furthermore, we conduct an analysis of the contributions of the different phases of the UCO processing to the resulting criteria. Finally, we introduce inaccurate or uncertain criteria values and criteria preferences to evaluate the uncertainty of the results.

Multi-criteria decision analysis deals with decision problems in which multiple criteria need to be considered. The criteria might be measured on different scales so that comparability is difficult. One approach to help the user to organize the problem and to reflect on his or her assessment on the decision is Measuring Attractiveness by a Categorical Based Evaluation TecHnique (MACBETH). Here the user needs to provide qualitative judgment about differences of attractiveness regarding pairs of options. MACBETH was implemented in the M-MACBETH software using the additive aggregation model. The present article introduces the software tool “AniFair” which combines the MACBETH approach with the Choquet integral as an aggregation function, because the Choquet integral enables the modeling of interaction between criteria. With the Choquet integral, the user can define constraints on the relative importance of criteria (Shapley value) and the interaction between criteria. In contrast to M-MACBETH, with every instance of “AniFair” the user is made available at least two aggregation level. “AniFair” provides Graphical User Interfaces for the entering of information. The software tool is introduced via an example from the Welfare Quality Assessment protocol for pigs. With this, “AniFair” is applied to real data that were collected from thirteen farms in Northern Germany by an animal welfare expert. The “AniFair” results enabled a division of the farms into five groups of comparable performance concerning the welfare principle “Good feeding”. Hereby, the results differed in how much the interaction between criteria contributed to the Choquet integral values. The shares varied from 5% to 55%. With this, the vulnerability of aggregation results towards relative importance of and interaction between criteria was stressed, as changes in the ranking due to the definition of constraints could be shown. All results were exported to human readable txt or csv files for further analyses, and advice could be given to the farmers on how to improve their welfare situation.

In our real life, the decision maker is always of bounded rationality under risk and uncertainty. The decision result also can be impacted by the risk appetite of the decision maker. However, the existing research works of three-way decisions rarely involve the risk appetite of the decision maker. To address this drawback and enrich the application, we introduce the risk appetite into three-way decisions. More specifically, under the dual hesitant fuzzy environment, we utilize TODIM (an acronym in Portuguese for Interactive Multi-Criteria Decision Making) as a valuable tool to handle the risk appetite character to construct risk appetite dual hesitant fuzzy three-way decisions. This study takes into account the risk appetite of decision maker and designs a series of decision analysis methodologies for three-way decisions. It can preferably satisfy the practical situation and vastly extend the range of applications. Firstly, based on the dual hesitant fuzzy loss functions, we mainly study dual hesitant fuzzy entropy and cross-entropy measures for determining the conditional probability. In this scenario, the conditional probability information can be objectively learned from the loss function matrix with the aid of the power average (PA) aggregated operator. Then, considering the different comparison methods of dual hesitant fuzzy elements (DHFEs), we further design two types of strategies to deduce risk appetite dual hesitant fuzzy three-way decisions in the framework of TODIM, i.e., Strategies 1 and 2, which mainly rely on the pairwise comparison of loss functions with DHFEs. Strategy 1 is designed based on the score function of DHFEs. Strategy 2 is the likelihood of DHFEs. Meanwhile, some properties are carefully investigated. Finally, the project investment evaluation of online peer-to-peer (P2P) is applied to illustrate and validate these proposed strategies.

USACE Institute for Water Resources (IWR) supplies forward-looking research and analysis to the Civil Works program. IWR investigates navigation of inland waterways across the United States and collects data. The data produced by the Navigation Investment Model (NIM) is not extensively used as an institutionally accepted norm to facilitate evaluation and decision making of infrastructure work packages. This research will present a new method for assessing work packages using a Utility to the Region and Nation (U2RN) metric which complements existing heuristic approaches with predictive data analysis techniques. Application of the new methodology will show how prioritization of work packages would significantly change when using data, and when applying a Monte Carlo simulation to determine future states. This paper provides a framework for waterborne investment that can be applied to the entire inland waterway and deep-sea infrastructure.

Purpose The purpose of this paper is to support companies’ risk-informed selection of a logistics solution to operate in China via cross-border e-commerce (CBEC). Design/methodology/approach Decision theory is applied to the recent field of CBEC. This theoretic setup involves a decision maker who must choose among a set of alternatives, whose consequences depend on uncertain factors (Savage, 1954). The study develops an activity-based model to calculate logistics costs in a deterministic setting. Simulations and probabilistic sensitivity analyses are later performed to evaluate the impact of uncertainty. Findings There are four main solutions to enter China, determined by the adopted international transport mean and the presence of a local warehouse. The most important risk factors affecting the choice of the logistics solution are change of CBEC regulation, product value, expected service level and demand level. Originality/value From a theoretical perspective, this study improves CBEC literature, so far characterised by descriptive papers, often lacking industry focus or empirical exploration. It also provides new application opportunities for decision theory, whereas previous contributions have proposed different theoretical approaches, such as transaction cost or institutional theory. From a practical viewpoint, the paper is the first to compare the costs of the main logistics solutions to sell online to China, by taking uncertainty into account. The results can be used to better understand the differences among solutions and identify the most critical parameters. Finally, this research provides some observations for policy implementation.

We introduce a new concept that enables a decision analyst to explore and quantify the benefits of decision alternatives that exceed the scope of a pre-posterior decision or value of information analysis. This new concept, namely, the expected value of sample information and action analysis, facilitates to examine decision alternatives that become only possible with additional knowledge. The concept is introduced by taking basis in proof load testing as a source of (pre-)posterior knowledge. Pre-posterior decision analysis is necessary in order to optimize the structural design through proof loading information. The application of the common value of information analysis and the new value of information and action analysis are demonstrated in a case study. (Less)

New advanced technologies have made it possible to deal with real-time schedule adjustments of public transport (PT) service. Indeed, using autonomous public transport vehicles (APTVs) prudently can result in significant improvements in the reliability, efficiency, and attractiveness of PT. The lack of human drivers and the feasible control strategies of the APTVs make it possible to offer a reliable, efficient and attractive service that can handle, in real time, the fluctuations in passenger demand. This work assumes that the APTVs of a single line are controlled centrally and are responsive to real-time requests from passengers. As the result, a real-time schedule can be constructed and communicated to the passengers. However, during the actual operations, the real-time demand will be fluctuated entailing schedule adjustments. These adjustments are optimized in this work using two operational strategies for the APTVs: holding and speed changing. The solution methodology proposed is based on time-space graphical techniques using multi-criteria decision analysis to minimize schedule changes as the primary objective, as well as to reduce travel time and active energy consumption. Two multi-objective models are developed for not-considered and considered vehicle capacity and then examined using a simulation framework. A case study from Auckland, New Zealand, is selected, which assumes that ordinary buses are replaced with APTVs. The results indicate that the strategy-based optimal schedule adjustments achieve a 100% elimination of schedule deviations compared with 33-minute and 13-minute deviations for the low-demand and high-demand scenarios of the ordinary PT service, respectively. The results of the simulated case study indicate that the incorporation of APTVs, along with prudent control, can result in a more attractive PT service.

Developing environmental conservation plans involves assessing trade-offs between the benefits and costs of conservation. The benefits of conservation can be established with ecological inventories or estimated based on previously collected information. Conducting ecological inventories can be costly, and the additional information may not justify these costs. To clarify the value of these inventories, we investigate the multiple criteria value of information associated with the acquisition of improved ecological data. This information can be useful when informing the decision maker to acquire better information. We extend the concept of the value of information to a multiple criteria perspective. We consider value of information for both monetary and biodiversity criteria and do not assume any fixed budget limits. Two illustrative cases are used describe this method of evaluating the multiple criteria value of information. In the first case, we numerically evaluate the multiple criteria value of information for a single forest stand. In the second case, we present a forest planning case with four stands that describes the complex interactions between the decision maker’s preference information and the potential inventory options available. These example cases highlight the importance of examining the trade-offs when making conservation decisions. We provide a definition for the multiple criteria value of information and demonstrate the potential application when conservation issues conflict with monetary issues.

Six problem formulations exist in multi-criteria decision analysis (MCDA): choice, sorting, ranking, description, elimination and design problems. MCDA methods are generally developed for choice or ranking problems. Recently, several methods have been adapted for sorting problems. However, they all assume that the criteria are independent, which is often not the case practically in real life. Therefore, this paper proposes a new sorting technique ANPSort, which can handle problems and challenges with interdependent criteria. Moreover, another practical limitation of ANP is that a high number of alternatives imply a large number of comparisons. In comparison, our proposed ANPSort requires far-less comparisons than ANP, which facilitates decision-making within large-scale problems. It further allows a structured, transparent and consistent evaluation integrating qualitative and quantitative criteria. In this paper, we contextualise and problematise this challenge and contribute through the lens of a practical case study in a higher education academic set-up specifically concentrating on a topical area of ‘researcher classification’, to illustrate our concept and approach.

Bayesian decision analysis is a useful method for risk management decisions, but is limited in its ability to consider severe uncertainty in knowledge, and value ambiguity in management objectives. We study the use of robust Bayesian decision analysis to handle problems where one or both of these issues arise. The robust Bayesian approach models severe uncertainty through bounds on probability distributions, and value ambiguity through bounds on utility functions. To incorporate data, standard Bayesian updating is applied on the entire set of distributions. To elicit our expert's utility representing the value of different management objectives, we use a modified version of the swing weighting procedure that can cope with severe value ambiguity. We demonstrate these methods on an environmental management problem to eradicate an alien invasive marmorkrebs recently discovered in Sweden, which needed a rapid response despite substantial knowledge gaps if the species was still present (i.e. severe uncertainty) and the need for difficult tradeoffs and competing interests (i.e. value ambiguity). We identify that the decision alternatives to drain the system and remove individuals in combination with dredging and sieving with or without a degradable biocide, or increasing pH, are consistently bad under the entire range of probability and utility bounds. This case study shows how robust Bayesian decision analysis provides a transparent methodology for integrating information in risk management problems where little data are available and/or where the tradeoffs ambiguous., Comment: 26 pages, 3 figures

This study evaluates the implementation costs of cloud-based booking platforms (CBBPs) to manage vehicle queues in warehouses and investigates how cloud-based booking platforms support inter-organizational logistics processes. To understand the impact of CBBPs, we interviewed logistics managers concerning the structure of the CBBP processes, salient platform features, and their effects on inter-organizational dynamics. In addition, we conducted an analytical modeling simulation to compute the economic impact of each investment scenario. Our results show that CBBP benefits, and their financial viability, are sensitive to the size of a company and the selected decision driver (cost vs. time optimization). Hence, our findings challenge the standard view that adopting digital technologies in inter-organizational settings is an easy task.

To date, there have been over 400 electric Vertical Takeoff and Landing (eVTOL) air vehicle designs that vary significantly in terms of design concepts, thrust type, air vehicle size or passenger seating capacity. Our study proposes a method for exploring ranking of passenger eVTOLs for vertiport network routes within large metropolitan regions. The method uses a benchmarking approach to indicate the eVTOLs most suitable to perform in the determined operational envelope. The study includes (i) a database, which was developed from open-source data that contains declared eVTOL performance characteristics, (ii) analysis of performance and limitations of eVTOLs, (iii) a multi-criteria decision analysis method for benchmarking technical specifications of eVTOLs, and (iv) an operational envelope design limited by a certain metropolitan area. To date, this has been the first attempt to map the passenger eVTOL family in its entirety. This research provides the UAM stakeholders with a generic tool that can be utilized in different metropolitan areas that anticipate the UAM deployment.

Made available in DSpace on 2022-04-28T19:44:54Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-10-01 Association for Progressive Communications Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) There is an increasing pressure by the community and customers forcing companies to insert environmental concerns in their practices. To help companies initiatives, the green bonds market was created. Our research question is “How to select bonds in a growing billion-dollar market?” This paper presents a multi-criteria decision analysis (MCDA) model to enable investors identify opportunities based not only in opinions, but grounded on objective facts. Analytic hierarchy process (AHP), complex proportional assessment (COPRAS), full consistency method (FUCOM), step-wise Weights Assessment Ratio Analysis (SWARA), and technique of order preference similarity to the ideal solution (TOPSIS) are MCDA methods applied in this paper. Top-fifteen green bonds ranked by specialized media were assessed with the proposed MCDA model. Criteria included the Environmental Performance Index (EPI) proposed by Yale University, and common financial indicators as assets, risks (β), and dividends. The new ranks from MCDA are compared each other and compared with the rank published by specialized media. Department of Production School of Engineering Universidade Estadual Paulista Júlio de Mesquita Filho Department of Industrial Engineering Corporación Universitária de la Costa, Calle 58 #55–66 Department of Production School of Engineering Universidade Estadual Paulista Júlio de Mesquita Filho

To make sophisticated decisions considering multiple conflicting criteria, such as choosing the location of a new facility is common for environmental managers. The environment and health of city inhabitants are greatly damaged if the selection of a site is wrong. In a metropolitan city, site selection for a trauma center is a quite tough decision taken by policymakers. Considering the minimal time, pollution and cost, this paper aims to overcome the challenges of selecting a site for a trauma center. A Multi-Criteria Decision Analysis process is developed which combines Analytic Hierarchy Process (AHP) with a utility-based method generic feature extraction approach (GFEA) to determine the optimum site for a new trauma center. In this study, 10 attributes among 5 different locations of Metropolitan city are considered as alternatives. AHP generates relative importance of the alternatives. The utility score is proposed using the GFEA method and the arithmetic points drawn by the alternatives make the final decision. Finally, a numerical study is presented to validate the possible alternative of the proposed methodology.

In fields on which decisions need to be taken including health, as we are seeing nowadays in the COVID-19 crisis, decision-makers face multiple criteria and results with a random component. In stochastic multicriteria decision-making models, the risk attitude of the decision maker is a relevant factor. Traditionally, the shape of a utility function is the only element that represents the decision maker’s risk attitude. The eduction process of multi-attribute utility functions implies some operational drawbacks, and it is not always easy. In this paper, we propose a new element with which the decision maker’s risk attitude can be implemented: the selection of the stochastic efficiency concept to be used during a decision analysis. We suggest representing the risk attitude as a conflict between two poles: risk neutral attitude, associated with best expectations, and risk aversion attitude, associated with a lower uncertainty. The Extended Goal Programming formulation has inspired the parameter that is introduced in a new risk attitude formulation. This parameter reflects the trade-off between the two classical poles with respect to risk attitude. Thus, we have produced a new stochastic efficiency concept that we call Compromise Efficiency.

Scientific and effective allocation of university assets is of great significance to improve the ability of university education guarantee. Decision tree is a decision analysis method based on the known probability of occurrence of various situations, which can calculate the probability that the expected value of net present value is greater than or equal to zero, evaluate the project risk and judge its feasibility by constructing a decision tree. It is a graphic method of intuitively using probability analysis. In machine learning, decision tree is a prediction model, which represents a mapping relationship between object attributes and object values. In this paper, by quantifying the data of state-owned assets in colleges and universities to form attribute sample categories, we use Cart decision tree algorithm which build a binary tree to learn data samples, construct prediction criteria, give correct classification of asset allocation, and provide a more reasonable solution for asset allocation.

Multi-Criteria Decision Analysis (MCDA) methods are increasingly used to aid decision-making for sustainable development. However, although uncertainty is present in all decision environments, dealing with incomplete and vague information in decision analysis remains a challenge. Our objective is to simplify the incorporation of uncertainty in the scoring of alternatives in MCDA processes. We present a modified ELimination and Choice Translating REality (ELECTRE) III model, in which the uncertainty in the performance scores is expressed as lower/upper bounds and then added to the model’s discrimination thresholds. Unlike other uncertainty approaches developed in the literature (such as those based on fuzzy set theory), our approach does not require additional knowledge apart from understanding the ELECTRE III model. To test its validity and suitability, we apply it for the evaluation of renewable energy resources for Turkey – hydro, wind, geothermal, solar, and biomass – under five main criteria: technological, technical, economic, environmental, and socio-politic. Our results indicate that wind energy is the best alternative for Turkey, followed by solar energy, which is in line with country’s Vision 2023 energy targets. This research has been partially funded by the Agència de Gestió d’Ajuts Universitaris i de Recerca de la Generalitat de Catalunya (Ref. 2017 SGR 1496). Peer Reviewed Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant

The growth of solar energy is projected to slow down during 2023–25 despite the fall in costs due to economic deceleration, reduced incentives, and market barriers including the lack of relevant and flexible energy project planning and decision-making tools. This study proposes a flexible and computationally simple multi-criteria decision analysis (MCDA)-based model that takes technical, financial, environmental, social and legal aspects of all project options as input and outputs a feasibility score for each option, which enables ranking the options and identifying the best alternative. The proposed model is applied to a real-world photovoltaic solar farm planned at a site in England and comprising nine different configurations formed by varying system capacity, energy storage option, mode of stakeholder, and network connections. The results of our study show that in this case the options without battery storage and a greater number of off-taker connections are more favorable than the options with battery storage. The analysis also shows that for the solar farm of the presented case study, ‘self-consumption fraction’ and ‘energy yield’, ‘net present value’, ‘life-cycle carbon emission reduction’, ‘ease of permit acquisition’ and ‘public approval’ are key sub-criteria for ‘technical’, ‘financial’, ‘environmental’, and ‘social and legal’ criteria, respectively. A sensitivity analysis was conducted to assess the confidence on the obtained solution, and a change in the first preference was noticed when ‘environmental’ and ‘social and legal’ aspects are given higher weight over ‘technical’ and ‘financial’ aspects. The results obtained are in line with the recommendations by experts, who carried out an independent feasibility analysis considering the same options.

The present study was an effort to assess the mitigation interventions carried out, so far, by the nations to fight the pandemic COVID-19. The novelty of the study was that it had considered the issue of pandemic mitigation strategy as a decision making problem. The performances of the twenty nations were to be ranked. The problem considered in the study was essentially a Multi-Criteria Decision Analysis (MCDA) problem. The available alternatives were the 20 countries and the 8 traits were the criteria. The Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) was used in the present study. The study used Entropy method for assignment of weights to all the criteria. The performance score obtained in respect of the countries considered in the study and the corresponding ranks indicated the relative performances of the countries in their efforts to mitigate the COVID-19 pandemic. The results show that New Zealand is the best performing country and India is the worst one. Brazil ranked 17th, while the rank of UK was 15. The performance of the USA stood at 18th position.

PurposeEvaluating the performance of supply chains is a convoluted task because of the complexity that is inextricably linked to the structure of the aforesaid chains. Therefore, the purpose of this paper is to present an integrated approach for evaluating and sizing real-life health-care supply chains in the presence of interval data.Design/methodology/approachTo achieve the objective, this paper illustrates an approach called Latin hypercube sampling by replacement (LHSR) to identify a set of precise data from the interval data; then the standard data envelopment analysis (DEA) models can be used to assess the relative efficiencies of the supply chains under evaluation. A certain level of data aggregation is suggested to improve the discriminatory power of the DEA models and an experimental design is conducted to size the supply chains under assessment.FindingsThe newly developed integrated methodology assists the decision-makers (DMs) in comparing their real-life supply chains against peers and sizing their resources to achieve a certain level of production.Practical implicationsThe proposed integrated DEA-based approach has been successfully implemented to suggest an appropriate structure to the actual public pharmaceutical supply chain in Morocco.Originality/valueThe originality of the proposed approach comes from the development of an integrated methodology to evaluate and size real-life health-care supply chains while taking into account interval data. This developed integrated technique certainly adds value to the health-care DMs for modelling their supply chains in today's world.

This paper puts forward a framework that assists managers to prepare action plans using attributes of Kano’s Model and to order them by utilizing multi-criteria decision analysis. The frame...

This paper concerns with a new multiple attribute decision making (MADM) model to cope with temporal performance of alternatives during different time periods. Dynamic MADM problems has grabbed remarkable attention of decision analysis community in recent years. In parallel with the recent advances in information technologies, firms steadily recognize the importance of data, and business analytics solutions is about to become standard business practice. Majority of the dynamic MADM literature deals with combining past and present data by means of aggregation operators. There is a research gap in developing data-driven methodologies to capture the patterns and trends in the historical data, and provide decision makers with meaningful insights in decision making practices. Analogous with the fact that style of decision making evolving from intuition-based to data-driven, this study proposes a new dynamic MADM model by learning of fuzzy cognitive maps (FCMs) to support decision makers in making informed decisions by considering future performance of alternatives. According to proposed model, Jaya algorithm, a simple and effective metaheuristic optimization method, is used to train FCMs to capture the patterns in historical data. Then, short-, medium-, and long-term future decision making matrices are generated. Finally, past, current and future decision making matrices are taken into consideration, and ranking of alternatives are obtained based on closeness coefficients. The proposed model is realized in a real-life supplier performance evaluation problem.

Multicriteria decision making (MCDM) methods are widely used in decision making practice. However, these methods are heuristic: they are based on some rules, the effectiveness and validity of which...